标题： MOFGPT：使用语言模型的金属有机框架生成设计 显示英文标题

标题： MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models

摘要： 具有特定应用性质的金属有机框架（MOFs）的发现仍然是材料化学中的一个核心挑战，因为它们的结构设计空间庞大且复杂。 尽管分子模拟和密度泛函理论（DFT）等常规计算筛选技术准确，但它们在大规模应用时计算成本过高。 机器学习通过利用数据驱动的方法加速材料发现提供了一个令人兴奋的替代方案。 MOFs 的复杂性，因其扩展的周期结构和多样的拓扑结构，为生成建模方法提供了机会和挑战。 为了解决这些挑战，我们提出了一种基于强化学习和变压器的框架，用于从头设计MOFs。 我们的方法的核心是MOFid，一种化学信息字符串表示，编码了连接性和拓扑结构，使生成模型能够扩展。 我们的流程包括三个组件：(1) 在MOFid序列上训练的生成式GPT模型，(2) 基于变压器的MOFormer属性预测器，以及(3) 通过属性引导奖励函数优化生成候选物的强化学习（RL）模块。 通过将属性反馈集成到序列生成中，我们的方法推动模型朝向可合成、拓扑有效的具有所需功能属性的MOFs发展。 这项工作展示了当大型语言模型与强化学习相结合时，在网状化学逆向设计和计算MOF发现的新领域解锁的潜力。 显示英文摘要

摘要： The discovery of Metal-Organic Frameworks (MOFs) with application-specific properties remains a central challenge in materials chemistry, owing to the immense size and complexity of their structural design space. Conventional computational screening techniques such as molecular simulations and density functional theory (DFT), while accurate, are computationally prohibitive at scale. Machine learning offers an exciting alternative by leveraging data-driven approaches to accelerate materials discovery. The complexity of MOFs, with their extended periodic structures and diverse topologies, creates both opportunities and challenges for generative modeling approaches. To address these challenges, we present a reinforcement learning-enhanced, transformer-based framework for the de novo design of MOFs. Central to our approach is MOFid, a chemically-informed string representation encoding both connectivity and topology, enabling scalable generative modeling. Our pipeline comprises three components: (1) a generative GPT model trained on MOFid sequences, (2) MOFormer, a transformer-based property predictor, and (3) a reinforcement learning (RL) module that optimizes generated candidates via property-guided reward functions. By integrating property feedback into sequence generation, our method drives the model toward synthesizable, topologically valid MOFs with desired functional attributes. This work demonstrates the potential of large language models, when coupled with reinforcement learning, to accelerate inverse design in reticular chemistry and unlock new frontiers in computational MOF discovery.